5 Critical Shortcomings of Traditional BI Tools
Business Intelligence (BI) tools have taken the business world by storm. According to new research, over 80% of executives believe that tools such as advanced visualization, dashboards, and reporting are critical tools when it comes to parsing data.
However, many end users aren’t bringing in those dashboards because they really use them, rather they are hoping to get a sense of security (incorrectly) that they will know everything about their business. This gets at the heart of some of the weaknesses of traditional business intelligence tools, where even at peak performance, traditional BI tools can miss key issues. There are several reasons why traditional BI tools can fall short of confronting problems they are being applied to solve.
Traditional BI Tools Don’t Dive Down into the Details
For reliable decision making, you need to embrace 100% of your organizational data (both internal and external). Your tools need to be able to quickly dive down into the details of data. When BI tool can’t get into the details of data and key metrics from throughout the organization, you won’t be in the position to make decisions when necessary.
One of the primary limitations of traditional BI tools is the lack of a granularity for the information parsed. For example, a case study from the Rubicon Project showcases how Rubicon’s life blood was its data, needing to quickly analyze billions of data points as real-time ad bidding auctions occurred within 40 milliseconds of one another.
The traditional BI tools that Rubicon used didn’t get to the details. While they were alerted for big events like when entire ad-bidding machines went down, if, however a client in Asia or Europe was trading at a much higher or lower rate than normal, their tools didn’t alert them to that level of detail. Their data analysts were forced to discern these trends through guesswork and intuition, often well after the anomalous trends had taken place.
Alert Storms Divert Users from Genuine Business Incidents
For data-driven organizations, BI operations teams often spend way too much time wading through a sea of alerts (sometimes referred to as the ‘alert storm’), most of which are just symptoms of the true cause of an issue. Pinpointing problems with traditional BI tools and trying to identify the difference between an alert on a meaningless trend deviation and a significant anomaly can be very difficult.
With traffic growing rapidly and changing variably, static thresholds on traditional BI tools need to be continuously increased often to the point of uselessness, constantly triggering alerts. Sometimes these are genuine issues, and sometimes they’re just noise – but traditional BI can’t tell the difference with either. While the constant noise on dashboards can seem comforting, it can cause issues since it’s not truly needed. With no limitation to the amount of data points collected or flexibility for setting alert notifications, it is difficult to differentiate redundant issues from significant ones. Good alert management begins with being able to scale the number of alerts, identifying the significant ones.
False Positives and Reactive Management
The metrics you want to focus on frequently are the handful of business KPIs everyone in your organization needs in front of them. Yet too many recurring alerts can create fatigue. Using traditional BI tools that employ standard static thresholds cause either too many false positives, or miss out on the important trends.
Analytics teams can spend excessive amounts of time calibrating alert thresholds manually, all the while losing money because thresholds are different over various channels. They may also miss the signs of performance issues when new services are implemented. As a business grows, more and more incidents can go undetected while trying to make sense of the massive volume of metrics. By creating a lot of dashboards and monitoring daily or weekly reports or by setting upper and lower alert thresholds for each metric leaves a lot of room for human error. Often these approaches fail, identifying too many anomalies (false positives) or not enough (false negatives).
Filtering out minor activity and highlighting the highest-priority risks can provide enough context to drastically diminish false positives and the burdens they place on overworked analyst teams.
Can’t Account for Seasonality
Some, but not all, of a company’s metrics will exhibit aspects of seasonality. This refers to the presence of cyclical patterns in time series data. The period of the cycle can span from hours to a full year or more. The main sources of seasonality could be climate, institutions, social habits and practices, or calendar. Seasonal patterns are changes we expect; they are part of the normal behavior of a given metric and thus must be included in the model of that metric. For some metrics, however, there are no seasonal patterns. And sometimes, multiple seasonal patterns are present in a time series. Since this can be mis-identified as outliers that might deserve attention, seasonal variability must be identified, filtered out and ignored.
Traditional BI tools are constantly collecting fluctuating data, usually calibrated towards a baseline level of business activity. Traditional BI tools aren’t designed to notice a 20% loss or gain, but to address pre-determined questions. The static thresholds used in traditional BI tools are meaningless for seasonal data, generating overwhelming alert-storms. Dashboards can’t keep up with these sudden spikes, where the data ends up being yesterday’s news.
Business Insight Latency Delays Problem Discovery
Problems come with the need for real-time analysis. This requires the ability to handle and process high velocity data in real time – a major challenge for traditional BI tools, which have data latency built into their architecture, as seen in setting up traditional BI dashboards. They don’t show status in real-time, so most users will only discover business problems after the fact. Business users need actionable insights based on the latest information, so minimizing latency gives analysts the most valid and up-to-date information available for accurate decision-making.
Disrupting the Static Nature of BI Tools
Traditional business intelligence (BI) tools are retrospective. Data may be just a few hours old, but in minutes, a transaction can fail, a customer can leave, or a something can crash. Data requires more than traditional approaches to detect and respond to business events as they happen. Due to these limitations, data analysts are only looking at a sub-set of the data, focusing on just a few key metrics, and struggling to get an integrated view of all business metrics.
It’s like using a flashlight to light up a football stadium. What you really need are floodlights. By taking in the full picture, you can make better decisions to impact business success and improve customer satisfaction.